Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	88,229
2	South Dakota	77,624
3	Iowa	62,607
4	Wisconsin	60,226
5	Nebraska	55,116
6	Utah	50,559
7	Idaho	48,694
8	Illinois	47,984
9	Montana	47,390
10	Tennessee	46,714
11	Mississippi	46,165
12	Arkansas	45,601
13	Alabama	45,579
14	Louisiana	45,154
15	Kansas	44,983
16	Wyoming	43,671
17	Missouri	43,371
18	Rhode Island	43,338
19	Minnesota	42,928
20	Florida	42,147
21	Nevada	40,941
22	Oklahoma	40,795
23	Indiana	40,231
24	Georgia	39,363
25	Arizona	39,093
26	Texas	38,824
27	South Carolina	38,737
28	Alaska	34,794
29	New Mexico	33,599
30	Kentucky	33,142
31	New Jersey	32,804
32	Colorado	30,948
33	North Carolina	30,726
34	Delaware	30,556
35	Michigan	30,293
36	New York	29,754
37	Maryland	28,483
38	Massachusetts	28,417
39	District of Columbia	27,580
40	Ohio	27,275
41	Connecticut	27,214
42	California	27,062
43	Virginia	24,466
44	Puerto Rico	24,274
45	Pennsylvania	22,454
46	West Virginia	20,242
47	Washington	18,714
48	Oregon	14,147
49	Hawaii	11,877
50	New Hampshire	11,582
51	Maine	7,081
52	Vermont	5,065

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Wyoming	1,601
2	Kansas	1,510
3	North Dakota	1,502
4	Nebraska	1,457
5	Rhode Island	1,378
6	South Dakota	1,239
7	Wisconsin	1,187
8	Iowa	1,185
9	Minnesota	1,091
10	Montana	1,089
11	New Mexico	993
12	Missouri	900
13	Michigan	889
14	Illinois	876
15	Utah	861
16	Idaho	834
17	Indiana	825
18	Alaska	804
19	Colorado	799
20	Connecticut	783
21	Tennessee	674
22	Oklahoma	614
23	Ohio	591
24	Kentucky	582
25	Nevada	580
26	Arkansas	505
27	West Virginia	486
28	Pennsylvania	479
29	Arizona	413
30	Texas	407
31	Louisiana	387
32	New Jersey	386
33	Alabama	385
34	Massachusetts	367
35	Mississippi	345
36	Delaware	325
37	Maryland	324
38	North Carolina	313
39	Washington	312
40	Florida	311
41	California	281
42	Virginia	268
43	Georgia	264
44	New Hampshire	264
45	South Carolina	255
46	New York	237
47	District of Columbia	230
48	Oregon	220
49	Puerto Rico	149
50	Vermont	145
51	Maine	142
52	Hawaii	46

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,875
2	New York	1,727
3	Massachusetts	1,509
4	Connecticut	1,341
5	Louisiana	1,330
6	Rhode Island	1,212
7	Mississippi	1,209
8	North Dakota	1,037
9	District of Columbia	942
10	Illinois	906
11	Arizona	874
12	Michigan	858
13	Georgia	831
14	Florida	825
15	South Carolina	812
16	Delaware	761
17	South Dakota	761
18	Indiana	755
19	Arkansas	753
20	Pennsylvania	745
21	Maryland	719
22	Texas	705
23	Alabama	682
24	Iowa	666
25	Nevada	632
26	New Mexico	615
27	Tennessee	586
28	Missouri	585
29	Minnesota	543
30	Montana	531
31	Wisconsin	504
32	Ohio	498
33	North Carolina	469
34	California	467
35	Colorado	463
36	Idaho	456
37	Kansas	455
38	Virginia	452
39	Nebraska	433
40	Kentucky	406
41	Oklahoma	396
42	New Hampshire	370
43	Washington	355
44	West Virginia	341
45	Puerto Rico	304
46	Wyoming	267
47	Utah	231
48	Oregon	187
49	Hawaii	156
50	Alaska	128
51	Maine	126
52	Vermont	96

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	21
2	Montana	13
3	Iowa	11
4	New Mexico	11
5	South Dakota	11
6	Arkansas	10
7	Wisconsin	10
8	Idaho	9
9	Rhode Island	9
10	Illinois	8
11	Indiana	8
12	Kansas	8
13	Nebraska	8
14	Tennessee	7
15	Alabama	6
16	Michigan	6
17	Minnesota	6
18	Mississippi	6
19	Missouri	6
20	Wyoming	6
21	Colorado	5
22	Kentucky	5
23	West Virginia	5
24	Connecticut	4
25	Pennsylvania	4
26	South Carolina	4
27	Texas	4
28	Florida	3
29	Georgia	3
30	Louisiana	3
31	Massachusetts	3
32	Nevada	3
33	New Jersey	3
34	North Carolina	3
35	Oklahoma	3
36	Puerto Rico	3
37	Arizona	2
38	Delaware	2
39	District of Columbia	2
40	Maryland	2
41	Ohio	2
42	Oregon	2
43	Utah	2
44	Virginia	2
45	Washington	2
46	California	1
47	Maine	1
48	New Hampshire	1
49	New York	1
50	Alaska	0
51	Hawaii	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Norton	Kansas	185,600	1	99
Lincoln	Arkansas	182,509	2	99
Bon Homme	South Dakota	180,119	3	99
Chattahoochee	Georgia	177,409	4	99
Buffalo	South Dakota	173,802	5	99
Davidson	Tennessee	59,862	430	86
Richland	South Carolina	46,178	920	70
York	South Carolina	30,202	1904	39
Orange	California	21,670	2432	22
Pierce	Washington	16,708	2647	15

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,208	1	99
Jerauld	South Dakota	6,458	2	99
Hancock	Georgia	5,321	3	99
Emporia city	Virginia	5,238	4	99
Kenedy	Texas	4,950	5	99
Richland	South Carolina	698	1109	64
Davidson	Tennessee	563	1383	55
Orange	California	481	1581	49
York	South Carolina	438	1709	45
Pierce	Washington	324	2064	34

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.